Generalized Energy Detection Under Generalized Noise Channels

TL;DR

This paper introduces a generalized energy detection method that models noise with the McLeish distribution, providing accurate performance metrics under diverse noise conditions and validating results through analysis and simulations.

Contribution

It is the first to apply the McLeish distribution for modeling noise in energy detection, deriving closed-form performance metrics for generalized noise environments.

Findings

Closed-form false-alarm and detection probabilities derived.

Validation of analytical results with simulations across SNR regimes.

Insights into GED system performance under noise uncertainty.

Abstract

Generalized energy detection (GED) is analytically studied when operates under fast-faded channels and in the presence of generalized noise. For the first time, the McLeish distribution is used to model the underlying noise, which is suitable for both non-Gaussian (impulsive) as well as classical Gaussian noise channels. Important performance metrics are presented in closed forms, such as the false-alarm and detection probabilities as well as the decision threshold. Analytical and simulation results are cross-compared validating the accuracy of the proposed approach in the entire signal-to-noise ratio regime. Finally, useful outcomes are extracted with respect to GED system settings under versatile noise environments and when noise uncertainty is present.